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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (11): 2258-2265    DOI: 10.3785/j.issn.1008-973X.2020.11.021
生物医学工程     
基于脑电信号预发作数据段选取的癫痫发作预测
王雅静1(),王群1,*(),李博闻1,刘志文1,朴媛媛2,遇涛2
1. 北京理工大学 信息与电子学院，北京 100081
2. 首都医科大学 宣武医院，北京 100053
Seizure prediction based on pre-ictal period selection of EEG signal
Ya-jing WANG1(),Qun WANG1,*(),Bo-wen LI1,Zhi-wen LIU1,Yuan-yuan PIAO2,Tao YU2
1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2. Xuanwu Hospital, Capital Medical University, Beijing 100053, China
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摘要：

为了提高癫痫发作预测的准确性，提出针对病患个体进行癫痫发作预测的算法，包括特征提取、预发作数据段选取、特征挑选与导联挑选. 算法采用半重叠的2 s窗对每个导联分别提取时频特征和空域特征. 从发作前期选择与发作间期相比具有最大线性可分性的连续10 min数据作为预发作数据段的有效正样本. 算法通过弹性网进行特征挑选，再基于所选特征通过贪婪算法选择有效导联，将有效导联的有效特征输入到分类器中. 将该算法在MIT公共头皮脑电数据库和宣武医院收集的数据集上进行测试，测试结果为：在MIT数据库上的击中率为95.76%，假阳性率为0.1073 h?1；在宣武医院数据集上的击中率为97.80%，假阳性率为0.0453 h?1. 结果表明，该算法具有较高的击中率和较低的假阳性率.
关键词： 癫痫发作预测;  头皮脑电图（sEEG）;  患者特异性;  特征挑选;  预发作数据段选取    
Abstract:

A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epileptic prediction, including feature extraction, pre-ictal period selection, feature selection and channel selection. Time-frequency features and spatial features were extracted from each channel by 2 s windows with 1 s overlap. A continuous 10 min data was selected as a valid positive sample of the pre-ictal period from segment before seizure onset, which achieved the maximum linear separability compared with the inter-ictal period. The effective features were selected by elastic net, then the selected effective features were used to select effective channels in greedy manner. The effective features of effective channels were input into classifier. The algorithm was tested on the scalp electroencephalogram (sEEG) from the MIT Physio database and the database collected in Xuanwu Hospital. The algorithm achieved a recall of 95.76% and a false positive rate of 0.1073 h?1 in MIT database, and a recall of 97.80% and a false positive rate of 0.0453 h?1 in Xuanwu Hospital database. Results show that the algorithm has high sensitivity and low false positive rate.
Key words: seizure prediction    scalp electroencephalogram (sEEG)    patient-specific    feature selection    pre-ictal period selection
收稿日期: 2019-11-25 出版日期: 2020-12-15
CLC:  TP 29  
基金资助: 国家自然科学基金资助项目（81771395）
通讯作者: 王群     E-mail: 3220180493@bit.edu.cn;wq99102@bit.edu.cn
作者简介: 王雅静（1997—），女，硕士生，从事医学信号处理研究.orcid.org/0000-0002-2027-9104. E-mail： 3220180493@bit.edu.cn
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引用本文:

王雅静,王群,李博闻,刘志文,朴媛媛,遇涛. 基于脑电信号预发作数据段选取的癫痫发作预测[J]. 浙江大学学报(工学版), 2020, 54(11): 2258-2265.

Ya-jing WANG,Qun WANG,Bo-wen LI,Zhi-wen LIU,Yuan-yuan PIAO,Tao YU. Seizure prediction based on pre-ictal period selection of EEG signal. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2258-2265.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.11.021    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I11/2258

图 1  数据处理流程
图 2  脑功能连接网络矩阵
编号 Ne nh R /% FPR /h?1
1 6 5.6 94.44 0.0430
5 4 4.0 100.00 0.1311
6 10 9.5 95.00 0.1741
7 3 3.0 100.00 0.1717
8 5 4.4 88.00 0.0419
9 4 3.5 87.50 0.1177
10 7 6.5 93.88 0.0884
11 3 3.0 100.00 0.1387
14 6 6.0 100.00 0.1195
16 5 4.4 88.00 0.1814
18 4 3.7 93.75 0.0916
19 2 2.0 100.00 0.0056
20 6 6.0 100.00 0.0487
22 3 3.0 100.00 0.1483
平均 ? ? 95.76 0.1073
表 1  MIT公共数据库预测结果
编号 T Ne nh R /% FPR /h?1
1 20 6 5.5 91.67 0.0412
2 22 6 4.8 80.56 0.0302
3 8 2 2.0 100.00 0.0412
4 16 3 3.0 100.00 0.1161
5 8 2 2.0 100.00 0.0527
6 11 4 4.0 100.00 0.0475
7 18 10 9.7 97.00 0.0280
8 8 2 2.0 100.00 0.0379
9 22 9 9.0 100.00 0.0726
10 19 6 6.0 100.00 0.0632
11 19 5 5.0 100.00 0.0485
12 17 4 4.0 100.00 0.0410
13 7 4 4.0 100.00 0.0465
14 13 4 4.0 100.00 0.0334
15 8 4 4.0 100.00 0.0489
16 16 7 7.0 100.00 0.0105
17 11 5 5.0 100.00 0.0324
18 27 9 8.0 88.89 0.0395
19 8 2 2.0 100.00 0.0289
平均 ? ? ? 97.80 0.0453
表 2  宣武医院数据集预测结果
图 3  预发作期与发作前10 min的特征对比
图 4  有无PPS对击中率的影响
图 5  有无PPS对假阳性率的影响
图 6  有无FCN对击中率的影响
图 7  有无FCN对假阳性率的影响
时间 方法 数据来源 发作次数 R /% FPR /h?1
2016 文献[32] MIT10位患者 31 77.00 0.1700
2013 文献[33] MIT10位患者 51 88.20 ?
2017 文献[34] MIT13位患者 45 86.67 0.3670
2017 文献[35] MIT24位患者 170 89.00 0.3900
2017 文献[36] MIT13位患者 64 81.20 0.1600
本研究 MIT17位患者 75 95.76 0.1073
表 3  基于MIT数据库的不同方法结果对比
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